The publication by F. Bovolo, L. Bruzzone, “A Split-Based Approach to Unsupervised Change Detection in Large-Size Multitemporal Images: Application to Tsunami-Damage Assessment”, IEEE Transactions on Geoscience and Remote Sensing, Vol. 45, No. 6, pp. 1658-1670, 2007 discloses a split-based approach for automatically detecting changes in a sequence of images. The method consists essentially in (i) splitting the image into sub-images; (ii) an analysis of each sub-image; and (iii) an automatic threshold-selection procedure. In step (ii) changes are identified by computing the histogram of difference values obtained from two sub-images that are acquired on the same geographical area at two different times. The sub-images are then sorted out according to their probability to contain a significant amount of changed pixels. The subset of the sub-images with a high probability to contain changes is selected and analysed in step (iii) according to a threshold-selection procedure applied separately to each sub-image or to the joint distribution of pixels that is obtained by merging all sub-images of the subset.
The publication S. Martinis, J. Kersten, A. Twele, “A fully automated TerraSAR-X based flood service”, ISPRS Journal of Photogrammetry and Remote Sensing, doi:10.1016/j.isprsjprs.2014.07.014, 2015 discloses an automatic image processing to identify flooded surfaces from Synthetic Aperture Radar (SAR) images. The processing of this teaching is also a split-based approach and is based on the backscatter statistics inferred from a single flood image to separate the “water” class from the others.
Both above mentioned teachings apply a split-based approach (SBA). This approach consists in tiling the image in sub-images of equal sizes and defining a threshold based on the histograms inferred from the different tiles. So far, SBA has been used to generate tiles of fixed size. The size is defined in an arbitrary way, using the SAR sensor resolution, the size of the scene and the percentage of the image occupied by the targeted class/population as indicators. However, this method is not efficient because i) the maximum size of the tile enabling the robust parameterization of the distribution function is unknown a priori and ii) the tiling process is not linked to the parameterization process of the distribution function.